Dare to Bare your Healthcare to the COVID scare

The COVID-19 pandemic has challenged humanity like nothing before. From the start, multidisciplinary teams of scientists have worked tirelessly creating and refining mathematical models to foretell the spread and effects of the virus. These teams include virologists, epidemiologists, infectious disease specialists, other physician specialists, public health experts, information technology experts, and statisticians. Some of the more prominent modeling groups work at Johns Hopkins University, European Centre for Disease Prevention and Control, the University of Washington Institute for Health Metrics and Evaluation, and the US Centers for Disease Control and Prevention.

These models for spread of the coronavirus that causes COVID-19, and incidence of the disease itself, incorporate a large number of input variables and of course generate output variables. As just one example of how these models work, consider effects of face mask use on COVID-19 mortality rate. A late June 2020 model from the University of Washington group predicted that 33,000 fewer Americans would die from COVID-19 by October 1 if 95% of Americans wore masks in public to reduce spread of the coronavirus.

A lot of work goes into creating and maintaining these models. Data are almost constantly being updated; variables are added, removed, or manipulated depending on circumstances; models are refined to address different types of questions; and model predictions are back-checked against reality. If one accepts Kahlil Gibran’s contention that “Work is love made visible,” these models may represent this idea well.

Variables included in the “model” depicted here are listed below. Some variables listed are not included in every published COVID-19 predictive model, and many other variables are not shown. Type of model is itself a variable.

Variables Included (in no particular order)

Serial interval Incubation period

Reproduction number Mutation rate

Dispersion factor Community spread

Infective dose Non-human vectors

Asymptomatic carriers Shedding

Nosocomial transmission Confirmed cases

Population demographics Geo-temporal spread

Hand-to-face self-inoculation Vulnerable populations

Gain of function Mobility patterns

Population demographics Post-recovery contagiousness

Surface contamination Attack rate